Deep‐Learning‐Enabled MXene‐Based Artificial Throat: Toward Sound Detection and Speech Recognition

Wearable sound detectors require strain sensors that are stretchable, sensitive, and capable of adhering conformably to the skin, and toward this end, 2D materials hold great promise. However, the vibration of vocal cords and muscle contraction are complex and changeable, which can compromise the sensing performance of devices. By combining deep learning and 2D MXenes, an MXene‐based sound detector is prepared successfully with improved recognition and sensitive response to pressure and vibration, which facilitate the production of a high‐recognition and resolution sound detector. By training and testing the deep learning network model with large amounts of data obtained by the MXene‐based sound detector, the long vowels and short vowels of human pronunciation are successfully recognized. The proposed scheme accelerates the application of artificial throat devices in biomedical fields and opens up practical applications in voice control, motion monitoring, and many other fields. The flexible sensor based on MXene can detect the movement signal of neck muscles caused by human pronunciation. Combining a large number of detected signals with convolutional neural networks can classify and recognize different pronunciation contents, which is conducive to the development of advanced wearable artificial larynx.